Nakada Kento, Taguchi Katsuyuki, Fung George S K, Amaya Kenji
Department of Mechanical and Environmental Informatics, Tokyo Institute of Technology School of Information Science and Engineering, Meguro 152-8550, Japan.
Division of Medical Imaging Physics, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287.
Med Phys. 2015 Sep;42(9):5329-41. doi: 10.1118/1.4927261.
Newly developed spectral computed tomography (CT) such as photon counting detector CT enables more accurate tissue-type identification through material decomposition technique. Many iterative reconstruction methods, including those developed for spectral CT, however, employ a regularization term whose penalty transition is designed using pixel value of CT image itself. Similarly, the tissue-type identification methods are then applied after reconstruction; thus, it is impossible to take into account probability distribution obtained from projection likelihood. The purpose of this work is to develop comprehensive image reconstruction and tissue-type identification algorithm which improves quality of both reconstructed image and tissue-type map.
The authors propose a new framework to jointly perform image reconstruction, material decomposition, and tissue-type identification for photon counting detector CT by applying maximum a posteriori estimation with voxel-based latent variables for the tissue types. The latent variables are treated using a voxel-based coupled Markov random field to describe the continuity and discontinuity of human organs and a set of Gaussian distributions to incorporate the statistical relation between the tissue types and their attenuation characteristics. The performance of the proposed method is quantitatively compared to that of filtered backprojection and a quadratic penalized likelihood method by 100 noise realization.
Results showed a superior trade-off between image noise and resolution to current reconstruction methods. The standard deviation (SD) and bias of reconstructed image were improved from quadratic penalized likelihood method: bias, -0.9 vs -0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of tissue-type identification was also improved from quadratic penalized likelihood method: 80.1% vs 86.9%.
The proposed method makes it possible not only to identify tissue types more accurately but also to reconstruct CT images with decreased noise and enhanced sharpness owing to the information about the tissue types.
新开发的光谱计算机断层扫描（CT），如光子计数探测器CT，能够通过物质分解技术更准确地识别组织类型。然而，许多迭代重建方法，包括为光谱CT开发的那些方法，都采用了一个正则化项，其惩罚转换是根据CT图像本身的像素值设计的。同样，组织类型识别方法是在重建后应用的；因此，无法考虑从投影似然性获得的概率分布。这项工作的目的是开发一种综合的图像重建和组织类型识别算法，以提高重建图像和组织类型图的质量。
作者提出了一个新的框架，通过应用基于体素的潜在变量对组织类型进行最大后验估计，来联合执行光子计数探测器CT的图像重建、物质分解和组织类型识别。潜在变量使用基于体素的耦合马尔可夫随机场来处理，以描述人体器官的连续性和不连续性，并使用一组高斯分布来纳入组织类型与其衰减特性之间的统计关系。通过100次噪声实现，将所提出方法的性能与滤波反投影法和二次惩罚似然法进行了定量比较。
结果表明，在所提出的方法在图像噪声和分辨率之间实现了比当前重建方法更好的权衡。重建图像的标准差（SD）和偏差相对于二次惩罚似然法有所改善：偏差，从-0.9亨氏单位（HU）降至-0.1 HU；SD，从46.8 HU降至27.4 HU。组织类型识别的准确性也相对于二次惩罚似然法有所提高：从80.1%提高到86.9%。
所提出的方法不仅能够更准确地识别组织类型，而且由于有关组织类型的信息，还能够重建噪声降低且清晰度增强的CT图像。
